Your First AI application¶

Going forward, AI algorithms will be incorporated into more and more everyday applications. For example, you might want to include an image classifier in a smart phone app. To do this, you'd use a deep learning model trained on hundreds of thousands of images as part of the overall application architecture. A large part of software development in the future will be using these types of models as common parts of applications.

In this project, you'll train an image classifier to recognize different species of flowers. You can imagine using something like this in a phone app that tells you the name of the flower your camera is looking at. In practice you'd train this classifier, then export it for use in your application. We'll be using this dataset from Oxford of 102 flower categories, you can see a few examples below.

The project is broken down into multiple steps:

  • Load the image dataset and create a pipeline.
  • Build and Train an image classifier on this dataset.
  • Use your trained model to perform inference on flower images.

We'll lead you through each part which you'll implement in Python.

When you've completed this project, you'll have an application that can be trained on any set of labeled images. Here your network will be learning about flowers and end up as a command line application. But, what you do with your new skills depends on your imagination and effort in building a dataset. For example, imagine an app where you take a picture of a car, it tells you what the make and model is, then looks up information about it. Go build your own dataset and make something new.

Install Datasets and Upgrade TensorFlow¶

To ensure we can download the latest version of the oxford_flowers102 dataset, let's first install both tensorflow-datasets and tfds-nightly.

  • tensorflow-datasets is the stable version that is released on a cadence of every few months
  • tfds-nightly is released every day and has the latest version of the datasets

We'll also upgrade TensorFlow to ensure we have a version that is compatible with the latest version of the dataset.

In [2]:
%pip --no-cache-dir install tensorflow-datasets --user
%pip --no-cache-dir install tfds-nightly --user
%pip --no-cache-dir install --upgrade tensorflow --user

Requirement already satisfied: tensorflow-datasets in /opt/conda/lib/python3.7/site-packages (1.2.0)
Requirement already satisfied: requests>=2.19.0 in /opt/conda/lib/python3.7/site-packages (from tensorflow-datasets) (2.22.0)
Requirement already satisfied: tqdm in /opt/conda/lib/python3.7/site-packages (from tensorflow-datasets) (4.36.1)
Requirement already satisfied: protobuf>=3.6.1 in /opt/conda/lib/python3.7/site-packages (from tensorflow-datasets) (3.11.2)
Requirement already satisfied: absl-py in /opt/conda/lib/python3.7/site-packages (from tensorflow-datasets) (0.8.1)
Requirement already satisfied: attrs in /opt/conda/lib/python3.7/site-packages (from tensorflow-datasets) (19.3.0)
Requirement already satisfied: future in /opt/conda/lib/python3.7/site-packages (from tensorflow-datasets) (0.18.2)
Requirement already satisfied: termcolor in /opt/conda/lib/python3.7/site-packages (from tensorflow-datasets) (1.1.0)
Requirement already satisfied: dill in /opt/conda/lib/python3.7/site-packages (from tensorflow-datasets) (0.3.1.1)
Requirement already satisfied: psutil in /opt/conda/lib/python3.7/site-packages (from tensorflow-datasets) (5.6.7)
Requirement already satisfied: numpy in /opt/conda/lib/python3.7/site-packages (from tensorflow-datasets) (1.17.4)
Requirement already satisfied: wrapt in /opt/conda/lib/python3.7/site-packages (from tensorflow-datasets) (1.11.2)
Requirement already satisfied: promise in /opt/conda/lib/python3.7/site-packages (from tensorflow-datasets) (2.2.1)
Requirement already satisfied: tensorflow-metadata in /opt/conda/lib/python3.7/site-packages (from tensorflow-datasets) (0.14.0)
Requirement already satisfied: six in /opt/conda/lib/python3.7/site-packages (from tensorflow-datasets) (1.12.0)
Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.7/site-packages (from requests>=2.19.0->tensorflow-datasets) (2019.11.28)
Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /opt/conda/lib/python3.7/site-packages (from requests>=2.19.0->tensorflow-datasets) (1.24.2)
Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /opt/conda/lib/python3.7/site-packages (from requests>=2.19.0->tensorflow-datasets) (3.0.4)
Requirement already satisfied: idna<2.9,>=2.5 in /opt/conda/lib/python3.7/site-packages (from requests>=2.19.0->tensorflow-datasets) (2.8)
Requirement already satisfied: setuptools in /opt/conda/lib/python3.7/site-packages (from protobuf>=3.6.1->tensorflow-datasets) (41.4.0)
Requirement already satisfied: googleapis-common-protos in /opt/conda/lib/python3.7/site-packages (from tensorflow-metadata->tensorflow-datasets) (1.6.0)
Note: you may need to restart the kernel to use updated packages.
Collecting tfds-nightly
  Downloading https://files.pythonhosted.org/packages/af/e2/7b12662617fc8731f125adb7c8430f6987442452508fb002052bfabd36c1/tfds_nightly-4.6.0.dev202209230045-py3-none-any.whl (4.7MB)
     |████████████████████████████████| 4.7MB 4.7MB/s eta 0:00:01
Collecting etils[epath]
  Downloading https://files.pythonhosted.org/packages/f4/45/f3ada048fd161fd71cc2b5e7b562584c8b14fada16258423a861fc54271b/etils-0.8.0-py3-none-any.whl (127kB)
     |████████████████████████████████| 133kB 43.1MB/s eta 0:00:01
Collecting importlib-resources; python_version < "3.9"
  Downloading https://files.pythonhosted.org/packages/d3/91/4df247dd4da18b72b5bbabe1fa2b85029c34e1d6f0afdd6329d15d6bf2b5/importlib_resources-5.9.0-py3-none-any.whl
Requirement already satisfied: termcolor in /opt/conda/lib/python3.7/site-packages (from tfds-nightly) (1.1.0)
Requirement already satisfied: requests>=2.19.0 in /opt/conda/lib/python3.7/site-packages (from tfds-nightly) (2.22.0)
Requirement already satisfied: tqdm in /opt/conda/lib/python3.7/site-packages (from tfds-nightly) (4.36.1)
Requirement already satisfied: absl-py in /opt/conda/lib/python3.7/site-packages (from tfds-nightly) (0.8.1)
Collecting protobuf>=3.12.2
  Downloading https://files.pythonhosted.org/packages/0f/3b/523691c80572934090a76c19b92fddbaf2a3c3947ae0fee75bc8a78ed8df/protobuf-4.21.6-cp37-abi3-manylinux2014_x86_64.whl (408kB)
     |████████████████████████████████| 409kB 48.9MB/s eta 0:00:01
Requirement already satisfied: six in /opt/conda/lib/python3.7/site-packages (from tfds-nightly) (1.12.0)
Requirement already satisfied: tensorflow-metadata in /opt/conda/lib/python3.7/site-packages (from tfds-nightly) (0.14.0)
Collecting toml
  Downloading https://files.pythonhosted.org/packages/44/6f/7120676b6d73228c96e17f1f794d8ab046fc910d781c8d151120c3f1569e/toml-0.10.2-py2.py3-none-any.whl
Collecting typing-extensions; python_version < "3.8"
  Downloading https://files.pythonhosted.org/packages/ed/d6/2afc375a8d55b8be879d6b4986d4f69f01115e795e36827fd3a40166028b/typing_extensions-4.3.0-py3-none-any.whl
Requirement already satisfied: dill in /opt/conda/lib/python3.7/site-packages (from tfds-nightly) (0.3.1.1)
Requirement already satisfied: promise in /opt/conda/lib/python3.7/site-packages (from tfds-nightly) (2.2.1)
Requirement already satisfied: numpy in /opt/conda/lib/python3.7/site-packages (from tfds-nightly) (1.17.4)
Requirement already satisfied: zipp; extra == "epath" in /opt/conda/lib/python3.7/site-packages (from etils[epath]->tfds-nightly) (0.6.0)
Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.7/site-packages (from requests>=2.19.0->tfds-nightly) (2019.11.28)
Requirement already satisfied: idna<2.9,>=2.5 in /opt/conda/lib/python3.7/site-packages (from requests>=2.19.0->tfds-nightly) (2.8)
Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /opt/conda/lib/python3.7/site-packages (from requests>=2.19.0->tfds-nightly) (3.0.4)
Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /opt/conda/lib/python3.7/site-packages (from requests>=2.19.0->tfds-nightly) (1.24.2)
Requirement already satisfied: googleapis-common-protos in /opt/conda/lib/python3.7/site-packages (from tensorflow-metadata->tfds-nightly) (1.6.0)
Requirement already satisfied: more-itertools in /opt/conda/lib/python3.7/site-packages (from zipp; extra == "epath"->etils[epath]->tfds-nightly) (8.0.2)
ERROR: tensorflow-metadata 0.14.0 has requirement protobuf<4,>=3.7, but you'll have protobuf 4.21.6 which is incompatible.
ERROR: importlib-resources 5.9.0 has requirement zipp>=3.1.0; python_version < "3.10", but you'll have zipp 0.6.0 which is incompatible.
Installing collected packages: importlib-resources, typing-extensions, etils, protobuf, toml, tfds-nightly
  WARNING: The script tfds is installed in '/root/.local/bin' which is not on PATH.
  Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.
Successfully installed etils-0.8.0 importlib-resources-5.9.0 protobuf-4.21.6 tfds-nightly-4.6.0.dev202209230045 toml-0.10.2 typing-extensions-4.3.0
Note: you may need to restart the kernel to use updated packages.
Collecting tensorflow
  Downloading https://files.pythonhosted.org/packages/3f/cd/9c217589c88448d67a4c755c4215cfae3e261e0af357ee81b9a5d7a96eda/tensorflow-2.10.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (578.0MB)
     |████████████████████████████████| 578.0MB 59.8MB/s eta 0:00:01| 433.3MB 51.5MB/s eta 0:00:03
Requirement already satisfied, skipping upgrade: gast<=0.4.0,>=0.2.1 in /opt/conda/lib/python3.7/site-packages (from tensorflow) (0.2.2)
Collecting keras-preprocessing>=1.1.1
  Downloading https://files.pythonhosted.org/packages/79/4c/7c3275a01e12ef9368a892926ab932b33bb13d55794881e3573482b378a7/Keras_Preprocessing-1.1.2-py2.py3-none-any.whl (42kB)
     |████████████████████████████████| 51kB 37.8MB/s eta 0:00:01
Collecting numpy>=1.20
  Downloading https://files.pythonhosted.org/packages/6d/ad/ff3b21ebfe79a4d25b4a4f8e5cf9fd44a204adb6b33c09010f566f51027a/numpy-1.21.6-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (15.7MB)
     |████████████████████████████████| 15.7MB 35.1MB/s eta 0:00:01
Requirement already satisfied, skipping upgrade: termcolor>=1.1.0 in /opt/conda/lib/python3.7/site-packages (from tensorflow) (1.1.0)
Collecting libclang>=13.0.0
  Downloading https://files.pythonhosted.org/packages/45/ff/2a6a2a536fc9a2893a6abfb8bbe317200db0475a0c474eccc9d47fa09d48/libclang-14.0.6-py2.py3-none-manylinux2010_x86_64.whl (14.1MB)
     |████████████████████████████████| 14.1MB 34.1MB/s 
Requirement already satisfied, skipping upgrade: wrapt>=1.11.0 in /opt/conda/lib/python3.7/site-packages (from tensorflow) (1.11.2)
Collecting keras<2.11,>=2.10.0
  Downloading https://files.pythonhosted.org/packages/f9/4d/dc255a437c9616b155e5bd55e325e092b7cdcb4652361d733ae051d40853/keras-2.10.0-py2.py3-none-any.whl (1.7MB)
     |████████████████████████████████| 1.7MB 30.1MB/s eta 0:00:01
Requirement already satisfied, skipping upgrade: typing-extensions>=3.6.6 in /root/.local/lib/python3.7/site-packages (from tensorflow) (4.3.0)
Collecting grpcio<2.0,>=1.24.3
  Downloading https://files.pythonhosted.org/packages/d2/5c/adda17c4ca2ab7d224c404d99854a940db6acd2c851e73774fd0b3868dac/grpcio-1.49.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.7MB)
     |████████████████████████████████| 4.7MB 35.1MB/s eta 0:00:01
Collecting absl-py>=1.0.0
  Downloading https://files.pythonhosted.org/packages/a5/b8/fc74a554a6fc7f26744c883ebfe405cf49c1f1320f13ee874aee47c70e1d/absl_py-1.2.0-py3-none-any.whl (123kB)
     |████████████████████████████████| 133kB 42.8MB/s eta 0:00:01
Requirement already satisfied, skipping upgrade: h5py>=2.9.0 in /opt/conda/lib/python3.7/site-packages (from tensorflow) (2.9.0)
Requirement already satisfied, skipping upgrade: setuptools in /opt/conda/lib/python3.7/site-packages (from tensorflow) (41.4.0)
Collecting protobuf<3.20,>=3.9.2
  Downloading https://files.pythonhosted.org/packages/4b/ec/12d7e2d69849601fc2ef3528df88a3642eee291ebaf7fa91c92b0b903ef9/protobuf-3.19.5-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.1MB)
     |████████████████████████████████| 1.1MB 29.2MB/s eta 0:00:01
Requirement already satisfied, skipping upgrade: google-pasta>=0.1.1 in /opt/conda/lib/python3.7/site-packages (from tensorflow) (0.1.8)
Requirement already satisfied, skipping upgrade: opt-einsum>=2.3.2 in /opt/conda/lib/python3.7/site-packages (from tensorflow) (3.1.0)
Collecting packaging
  Downloading https://files.pythonhosted.org/packages/05/8e/8de486cbd03baba4deef4142bd643a3e7bbe954a784dc1bb17142572d127/packaging-21.3-py3-none-any.whl (40kB)
     |████████████████████████████████| 40kB 31.8MB/s eta 0:00:01
Collecting tensorflow-io-gcs-filesystem>=0.23.1
  Downloading https://files.pythonhosted.org/packages/79/67/e0e1869e4a296ec5922752a68a351220f529fdd84acc668e23c75356f4d1/tensorflow_io_gcs_filesystem-0.27.0-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (2.4MB)
     |████████████████████████████████| 2.4MB 33.9MB/s eta 0:00:01
Collecting flatbuffers>=2.0
  Downloading https://files.pythonhosted.org/packages/d7/0d/b5bfb553a6ac66d6ec2b6d7f1e814a908fba7188356ac94bb36ae3d905c3/flatbuffers-2.0.7-py2.py3-none-any.whl
Collecting tensorboard<2.11,>=2.10
  Downloading https://files.pythonhosted.org/packages/6b/42/e271c40c84c250b52fa460fda970899407c837a2049c53969f37e404b1f6/tensorboard-2.10.0-py3-none-any.whl (5.9MB)
     |████████████████████████████████| 5.9MB 40.2MB/s eta 0:00:01
Collecting tensorflow-estimator<2.11,>=2.10.0
  Downloading https://files.pythonhosted.org/packages/b6/05/9d7f3a6c783669cba36a6eb4555d0c73a516eee935dde6176dfb8512f94e/tensorflow_estimator-2.10.0-py2.py3-none-any.whl (438kB)
     |████████████████████████████████| 440kB 37.9MB/s eta 0:00:01
Requirement already satisfied, skipping upgrade: six>=1.12.0 in /opt/conda/lib/python3.7/site-packages (from tensorflow) (1.12.0)
Collecting astunparse>=1.6.0
  Downloading https://files.pythonhosted.org/packages/2b/03/13dde6512ad7b4557eb792fbcf0c653af6076b81e5941d36ec61f7ce6028/astunparse-1.6.3-py2.py3-none-any.whl
Requirement already satisfied, skipping upgrade: pyparsing!=3.0.5,>=2.0.2 in /opt/conda/lib/python3.7/site-packages (from packaging->tensorflow) (2.4.6)
Collecting tensorboard-data-server<0.7.0,>=0.6.0
  Downloading https://files.pythonhosted.org/packages/60/f9/802efd84988bffd9f644c03b6e66fde8e76c3aa33db4279ddd11c5d61f4b/tensorboard_data_server-0.6.1-py3-none-manylinux2010_x86_64.whl (4.9MB)
     |████████████████████████████████| 4.9MB 37.2MB/s eta 0:00:01
Collecting tensorboard-plugin-wit>=1.6.0
  Downloading https://files.pythonhosted.org/packages/e0/68/e8ecfac5dd594b676c23a7f07ea34c197d7d69b3313afdf8ac1b0a9905a2/tensorboard_plugin_wit-1.8.1-py3-none-any.whl (781kB)
     |████████████████████████████████| 788kB 43.7MB/s eta 0:00:01
Requirement already satisfied, skipping upgrade: wheel>=0.26 in /opt/conda/lib/python3.7/site-packages (from tensorboard<2.11,>=2.10->tensorflow) (0.33.6)
Collecting google-auth-oauthlib<0.5,>=0.4.1
  Downloading https://files.pythonhosted.org/packages/b1/0e/0636cc1448a7abc444fb1b3a63655e294e0d2d49092dc3de05241be6d43c/google_auth_oauthlib-0.4.6-py2.py3-none-any.whl
Collecting werkzeug>=1.0.1
  Downloading https://files.pythonhosted.org/packages/c8/27/be6ddbcf60115305205de79c29004a0c6bc53cec814f733467b1bb89386d/Werkzeug-2.2.2-py3-none-any.whl (232kB)
     |████████████████████████████████| 235kB 44.2MB/s eta 0:00:01
Collecting google-auth<3,>=1.6.3
  Downloading https://files.pythonhosted.org/packages/be/65/3126b832ae3a8c7446820a9603b4b73e39ade43995922665f128f5aa53ab/google_auth-2.11.1-py2.py3-none-any.whl (167kB)
     |████████████████████████████████| 174kB 44.2MB/s eta 0:00:01
Requirement already satisfied, skipping upgrade: requests<3,>=2.21.0 in /opt/conda/lib/python3.7/site-packages (from tensorboard<2.11,>=2.10->tensorflow) (2.22.0)
Requirement already satisfied, skipping upgrade: markdown>=2.6.8 in /opt/conda/lib/python3.7/site-packages (from tensorboard<2.11,>=2.10->tensorflow) (3.1.1)
Collecting requests-oauthlib>=0.7.0
  Downloading https://files.pythonhosted.org/packages/6f/bb/5deac77a9af870143c684ab46a7934038a53eb4aa975bc0687ed6ca2c610/requests_oauthlib-1.3.1-py2.py3-none-any.whl
Collecting MarkupSafe>=2.1.1
  Downloading https://files.pythonhosted.org/packages/9f/83/b221ce5a0224f409b9f02b0dc6cb0b921c46033f4870d64fa3e8a96af701/MarkupSafe-2.1.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Collecting pyasn1-modules>=0.2.1
  Downloading https://files.pythonhosted.org/packages/95/de/214830a981892a3e286c3794f41ae67a4495df1108c3da8a9f62159b9a9d/pyasn1_modules-0.2.8-py2.py3-none-any.whl (155kB)
     |████████████████████████████████| 163kB 49.0MB/s eta 0:00:01
Collecting rsa<5,>=3.1.4; python_version >= "3.6"
  Downloading https://files.pythonhosted.org/packages/49/97/fa78e3d2f65c02c8e1268b9aba606569fe97f6c8f7c2d74394553347c145/rsa-4.9-py3-none-any.whl
Collecting cachetools<6.0,>=2.0.0
  Downloading https://files.pythonhosted.org/packages/68/aa/5fc646cae6e997c3adf3b0a7e257cda75cff21fcba15354dffd67789b7bb/cachetools-5.2.0-py3-none-any.whl
Requirement already satisfied, skipping upgrade: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /opt/conda/lib/python3.7/site-packages (from requests<3,>=2.21.0->tensorboard<2.11,>=2.10->tensorflow) (1.24.2)
Requirement already satisfied, skipping upgrade: chardet<3.1.0,>=3.0.2 in /opt/conda/lib/python3.7/site-packages (from requests<3,>=2.21.0->tensorboard<2.11,>=2.10->tensorflow) (3.0.4)
Requirement already satisfied, skipping upgrade: certifi>=2017.4.17 in /opt/conda/lib/python3.7/site-packages (from requests<3,>=2.21.0->tensorboard<2.11,>=2.10->tensorflow) (2019.11.28)
Requirement already satisfied, skipping upgrade: idna<2.9,>=2.5 in /opt/conda/lib/python3.7/site-packages (from requests<3,>=2.21.0->tensorboard<2.11,>=2.10->tensorflow) (2.8)
Collecting oauthlib>=3.0.0
  Downloading https://files.pythonhosted.org/packages/92/bb/d669baf53d4ffe081dab80aad93c5c79f84eeac885dd31507c8c055a98d5/oauthlib-3.2.1-py3-none-any.whl (151kB)
     |████████████████████████████████| 153kB 43.3MB/s eta 0:00:01
Collecting pyasn1<0.5.0,>=0.4.6
  Downloading https://files.pythonhosted.org/packages/62/1e/a94a8d635fa3ce4cfc7f506003548d0a2447ae76fd5ca53932970fe3053f/pyasn1-0.4.8-py2.py3-none-any.whl (77kB)
     |████████████████████████████████| 81kB 41.7MB/s eta 0:00:01
Installing collected packages: numpy, keras-preprocessing, libclang, keras, grpcio, absl-py, protobuf, packaging, tensorflow-io-gcs-filesystem, flatbuffers, tensorboard-data-server, tensorboard-plugin-wit, pyasn1, pyasn1-modules, rsa, cachetools, google-auth, oauthlib, requests-oauthlib, google-auth-oauthlib, MarkupSafe, werkzeug, tensorboard, tensorflow-estimator, astunparse, tensorflow
  WARNING: The scripts f2py, f2py3 and f2py3.7 are installed in '/root/.local/bin' which is not on PATH.
  Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.
  Found existing installation: protobuf 4.21.6
    Uninstalling protobuf-4.21.6:
      Successfully uninstalled protobuf-4.21.6
  WARNING: The scripts pyrsa-decrypt, pyrsa-encrypt, pyrsa-keygen, pyrsa-priv2pub, pyrsa-sign and pyrsa-verify are installed in '/root/.local/bin' which is not on PATH.
  Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.
  WARNING: The script google-oauthlib-tool is installed in '/root/.local/bin' which is not on PATH.
  Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.
  WARNING: The script tensorboard is installed in '/root/.local/bin' which is not on PATH.
  Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.
  WARNING: The scripts estimator_ckpt_converter, import_pb_to_tensorboard, saved_model_cli, tensorboard, tf_upgrade_v2, tflite_convert, toco and toco_from_protos are installed in '/root/.local/bin' which is not on PATH.
  Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.
Successfully installed MarkupSafe-2.1.1 absl-py-1.2.0 astunparse-1.6.3 cachetools-5.2.0 flatbuffers-2.0.7 google-auth-2.11.1 google-auth-oauthlib-0.4.6 grpcio-1.49.1 keras-2.10.0 keras-preprocessing-1.1.2 libclang-14.0.6 numpy-1.21.6 oauthlib-3.2.1 packaging-21.3 protobuf-3.19.5 pyasn1-0.4.8 pyasn1-modules-0.2.8 requests-oauthlib-1.3.1 rsa-4.9 tensorboard-2.10.0 tensorboard-data-server-0.6.1 tensorboard-plugin-wit-1.8.1 tensorflow-2.10.0 tensorflow-estimator-2.10.0 tensorflow-io-gcs-filesystem-0.27.0 werkzeug-2.2.2
Note: you may need to restart the kernel to use updated packages.

After the above installations have finished be sure to restart the kernel. You can do this by going to Kernel > Restart.

In [1]:
# Import TensorFlow 
import tensorflow as tf
import tensorflow_datasets as tfds
import tensorflow_hub as hub

# Ignore some warnings that are not relevant (you can remove this if you prefer)
import warnings
warnings.filterwarnings('ignore')
In [2]:
# TODO: Make all other necessary imports.
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
In [3]:
# Some other recommended settings:
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
tfds.disable_progress_bar()

Load the Dataset¶

Here you'll use tensorflow_datasets to load the Oxford Flowers 102 dataset. This dataset has 3 splits: 'train', 'test', and 'validation'. You'll also need to make sure the training data is normalized and resized to 224x224 pixels as required by the pre-trained networks.

The validation and testing sets are used to measure the model's performance on data it hasn't seen yet, but you'll still need to normalize and resize the images to the appropriate size.

In [4]:
# TODO: Load the dataset with TensorFlow Datasets. Hint: use tfds.load()

# TODO: Create a training set, a validation set and a test set.
(training_set,validation_set,test_set), dataset_info = tfds.load('oxford_flowers102', split = ['train', 'validation','test'],as_supervised = True, with_info = True)

Downloading and preparing dataset 328.90 MiB (download: 328.90 MiB, generated: 331.34 MiB, total: 660.25 MiB) to /root/tensorflow_datasets/oxford_flowers102/2.1.1...
Dataset oxford_flowers102 downloaded and prepared to /root/tensorflow_datasets/oxford_flowers102/2.1.1. Subsequent calls will reuse this data.

Explore the Dataset¶

In [5]:
dataset_info
Out[5]:
tfds.core.DatasetInfo(
    name='oxford_flowers102',
    full_name='oxford_flowers102/2.1.1',
    description="""
    The Oxford Flowers 102 dataset is a consistent of 102 flower categories commonly occurring
    in the United Kingdom. Each class consists of between 40 and 258 images. The images have
    large scale, pose and light variations. In addition, there are categories that have large
    variations within the category and several very similar categories.
    
    The dataset is divided into a training set, a validation set and a test set.
    The training set and validation set each consist of 10 images per class (totalling 1020 images each).
    The test set consists of the remaining 6149 images (minimum 20 per class).
    
    Note: The dataset by default comes with a test size larger than the train
    size. For more info see this [issue](https://github.com/tensorflow/datasets/issues/3022).
    """,
    homepage='https://www.robots.ox.ac.uk/~vgg/data/flowers/102/',
    data_path='/tmp/tmpkwo3piv8tfds',
    file_format=tfrecord,
    download_size=328.90 MiB,
    dataset_size=331.34 MiB,
    features=FeaturesDict({
        'file_name': Text(shape=(), dtype=tf.string),
        'image': Image(shape=(None, None, 3), dtype=tf.uint8),
        'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=102),
    }),
    supervised_keys=('image', 'label'),
    disable_shuffling=False,
    splits={
        'test': <SplitInfo num_examples=6149, num_shards=2>,
        'train': <SplitInfo num_examples=1020, num_shards=1>,
        'validation': <SplitInfo num_examples=1020, num_shards=1>,
    },
    citation="""@InProceedings{Nilsback08,
       author = "Nilsback, M-E. and Zisserman, A.",
       title = "Automated Flower Classification over a Large Number of Classes",
       booktitle = "Proceedings of the Indian Conference on Computer Vision, Graphics and Image Processing",
       year = "2008",
       month = "Dec"
    }""",
)
In [6]:
# TODO: Get the number of examples in each set from the dataset info.
print("Training set contains %d images"%(dataset_info.splits['train'].num_examples))
print("Validation set contains %d images"%(dataset_info.splits['validation'].num_examples))
print("Testing set contains %d images"%(dataset_info.splits['test'].num_examples))
# TODO: Get the number of classes in the dataset from the dataset info.
print("Number of classes in the dataset: %d"%(dataset_info.features['label'].num_classes))      

Training set contains 1020 images
Validation set contains 1020 images
Testing set contains 6149 images
Number of classes in the dataset: 102
In [7]:
# TODO: Print the shape and corresponding label of 3 images in the training set.
for image, label in training_set.take(3):
    image = image.numpy()
    label = label.numpy()
    print(image.shape, "-", label)

(500, 667, 3) - 72
(500, 666, 3) - 84
(670, 500, 3) - 70
In [8]:
# TODO: Plot 1 image from the training set. 
for image, label in training_set.take(1):
    image = image.numpy()
    label = label.numpy()
    print(image.shape)
plt.title(label)
plt.imshow(image)
plt.show()
# Set the title of the plot to the corresponding image label. 

(500, 667, 3)

Label Mapping¶

You'll also need to load in a mapping from label to category name. You can find this in the file label_map.json. It's a JSON object which you can read in with the json module. This will give you a dictionary mapping the integer coded labels to the actual names of the flowers.

In [9]:
import json 

with open('label_map.json', 'r') as f:
    class_names = json.load(f)
In [34]:
class_names
Out[34]:
{'21': 'fire lily',
 '3': 'canterbury bells',
 '45': 'bolero deep blue',
 '1': 'pink primrose',
 '34': 'mexican aster',
 '27': 'prince of wales feathers',
 '7': 'moon orchid',
 '16': 'globe-flower',
 '25': 'grape hyacinth',
 '26': 'corn poppy',
 '79': 'toad lily',
 '39': 'siam tulip',
 '24': 'red ginger',
 '67': 'spring crocus',
 '35': 'alpine sea holly',
 '32': 'garden phlox',
 '10': 'globe thistle',
 '6': 'tiger lily',
 '93': 'ball moss',
 '33': 'love in the mist',
 '9': 'monkshood',
 '102': 'blackberry lily',
 '14': 'spear thistle',
 '19': 'balloon flower',
 '100': 'blanket flower',
 '13': 'king protea',
 '49': 'oxeye daisy',
 '15': 'yellow iris',
 '61': 'cautleya spicata',
 '31': 'carnation',
 '64': 'silverbush',
 '68': 'bearded iris',
 '63': 'black-eyed susan',
 '69': 'windflower',
 '62': 'japanese anemone',
 '20': 'giant white arum lily',
 '38': 'great masterwort',
 '4': 'sweet pea',
 '86': 'tree mallow',
 '101': 'trumpet creeper',
 '42': 'daffodil',
 '22': 'pincushion flower',
 '2': 'hard-leaved pocket orchid',
 '54': 'sunflower',
 '66': 'osteospermum',
 '70': 'tree poppy',
 '85': 'desert-rose',
 '99': 'bromelia',
 '87': 'magnolia',
 '5': 'english marigold',
 '92': 'bee balm',
 '28': 'stemless gentian',
 '97': 'mallow',
 '57': 'gaura',
 '40': 'lenten rose',
 '47': 'marigold',
 '59': 'orange dahlia',
 '48': 'buttercup',
 '55': 'pelargonium',
 '36': 'ruby-lipped cattleya',
 '91': 'hippeastrum',
 '29': 'artichoke',
 '71': 'gazania',
 '90': 'canna lily',
 '18': 'peruvian lily',
 '98': 'mexican petunia',
 '8': 'bird of paradise',
 '30': 'sweet william',
 '17': 'purple coneflower',
 '52': 'wild pansy',
 '84': 'columbine',
 '12': "colt's foot",
 '11': 'snapdragon',
 '96': 'camellia',
 '23': 'fritillary',
 '50': 'common dandelion',
 '44': 'poinsettia',
 '53': 'primula',
 '72': 'azalea',
 '65': 'californian poppy',
 '80': 'anthurium',
 '76': 'morning glory',
 '37': 'cape flower',
 '56': 'bishop of llandaff',
 '60': 'pink-yellow dahlia',
 '82': 'clematis',
 '58': 'geranium',
 '75': 'thorn apple',
 '41': 'barbeton daisy',
 '95': 'bougainvillea',
 '43': 'sword lily',
 '83': 'hibiscus',
 '78': 'lotus lotus',
 '88': 'cyclamen',
 '94': 'foxglove',
 '81': 'frangipani',
 '74': 'rose',
 '89': 'watercress',
 '73': 'water lily',
 '46': 'wallflower',
 '77': 'passion flower',
 '51': 'petunia'}
In [10]:
# TODO: Plot 1 image from the training set. Set the title 
# of the plot to the corresponding class name. 
plt.title(class_names[str(label)])
plt.imshow(image)
plt.show()

Create Pipeline¶

In [11]:
# TODO: Create a pipeline for each set.
batch_size = 32
image_size = 224

def format_image(image, label):
    image = tf.cast(image, tf.float32)
    image = tf.image.resize(image, (image_size, image_size))
    image /= 255
    return image, label

training_batches = training_set.shuffle(23262*60//4).map(format_image).batch(batch_size).prefetch(1)
validation_batches = validation_set.map(format_image).batch(batch_size).prefetch(1)
testing_batches = test_set.map(format_image).batch(batch_size).prefetch(1)

WARNING:tensorflow:AutoGraph could not transform <function format_image at 0x7fdd247437a0> and will run it as-is.
Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.
Cause: 'arguments' object has no attribute 'posonlyargs'
To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert

WARNING:tensorflow:AutoGraph could not transform <function format_image at 0x7fdd247437a0> and will run it as-is.
Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.
Cause: 'arguments' object has no attribute 'posonlyargs'
To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert

WARNING: AutoGraph could not transform <function format_image at 0x7fdd247437a0> and will run it as-is.
Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.
Cause: 'arguments' object has no attribute 'posonlyargs'
To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert

Build and Train the Classifier¶

Now that the data is ready, it's time to build and train the classifier. You should use the MobileNet pre-trained model from TensorFlow Hub to get the image features. Build and train a new feed-forward classifier using those features.

We're going to leave this part up to you. If you want to talk through it with someone, chat with your fellow students!

Refer to the rubric for guidance on successfully completing this section. Things you'll need to do:

  • Load the MobileNet pre-trained network from TensorFlow Hub.
  • Define a new, untrained feed-forward network as a classifier.
  • Train the classifier.
  • Plot the loss and accuracy values achieved during training for the training and validation set.
  • Save your trained model as a Keras model.

We've left a cell open for you below, but use as many as you need. Our advice is to break the problem up into smaller parts you can run separately. Check that each part is doing what you expect, then move on to the next. You'll likely find that as you work through each part, you'll need to go back and modify your previous code. This is totally normal!

When training make sure you're updating only the weights of the feed-forward network. You should be able to get the validation accuracy above 70% if you build everything right.

Note for Workspace users: One important tip if you're using the workspace to run your code: To avoid having your workspace disconnect during the long-running tasks in this notebook, please read in the earlier page in this lesson called Intro to GPU Workspaces about Keeping Your Session Active. You'll want to include code from the workspace_utils.py module. Also, If your model is over 1 GB when saved as a checkpoint, there might be issues with saving backups in your workspace. If your saved checkpoint is larger than 1 GB (you can open a terminal and check with ls -lh), you should reduce the size of your hidden layers and train again.

In [18]:
# TODO: Build and train your network.
url = 'https://tfhub.dev/google/tf2-preview/mobilenet_v2/feature_vector/4'
feature_extractor = hub.KerasLayer(url, input_shape = (image_size, image_size, 3))
feature_extractor.trainable = False

model = tf.keras.Sequential ([
    feature_extractor, 
    tf.keras.layers.Dense(102, activation = 'softmax')
])
In [19]:
# TODO: Plot the loss and accuracy values achieved during training for the training and validation set.
model.compile(optimizer = 'adam',
             loss = 'sparse_categorical_crossentropy',
             metrics = ['accuracy'])
In [20]:
# Monitoring validation loss
early_stopping = tf.keras.callbacks.EarlyStopping(monitor = 'val_loss', patience = 10)
# Saving the best model
save_best = tf.keras.callbacks.ModelCheckpoint('./best_model.h5',
                                              monitor = 'val_loss',
                                              save_best_only = True)
model.summary()

Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 keras_layer_2 (KerasLayer)  (None, 1280)              2257984   
                                                                 
 dense_1 (Dense)             (None, 102)               130662    
                                                                 
=================================================================
Total params: 2,388,646
Trainable params: 130,662
Non-trainable params: 2,257,984
_________________________________________________________________
In [21]:
history = model.fit(training_batches, 
                    epochs = 10, 
                    validation_data = validation_batches, 
                    callbacks = [early_stopping,save_best])

Epoch 1/10
WARNING:tensorflow:AutoGraph could not transform <function Model.make_train_function.<locals>.train_function at 0x7fdd246b8d40> and will run it as-is.
Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.
Cause: 'arguments' object has no attribute 'posonlyargs'
To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert

WARNING:tensorflow:AutoGraph could not transform <function Model.make_train_function.<locals>.train_function at 0x7fdd246b8d40> and will run it as-is.
Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.
Cause: 'arguments' object has no attribute 'posonlyargs'
To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert

WARNING: AutoGraph could not transform <function Model.make_train_function.<locals>.train_function at 0x7fdd246b8d40> and will run it as-is.
Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.
Cause: 'arguments' object has no attribute 'posonlyargs'
To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert
32/32 [==============================] - ETA: 0s - loss: 4.2351 - accuracy: 0.1225WARNING:tensorflow:AutoGraph could not transform <function Model.make_test_function.<locals>.test_function at 0x7fdcd4266a70> and will run it as-is.
Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.
Cause: 'arguments' object has no attribute 'posonlyargs'
To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert

WARNING:tensorflow:AutoGraph could not transform <function Model.make_test_function.<locals>.test_function at 0x7fdcd4266a70> and will run it as-is.
Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.
Cause: 'arguments' object has no attribute 'posonlyargs'
To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert

WARNING: AutoGraph could not transform <function Model.make_test_function.<locals>.test_function at 0x7fdcd4266a70> and will run it as-is.
Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.
Cause: 'arguments' object has no attribute 'posonlyargs'
To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert
32/32 [==============================] - 49s 1s/step - loss: 4.2351 - accuracy: 0.1225 - val_loss: 3.0544 - val_accuracy: 0.3863
Epoch 2/10
32/32 [==============================] - 44s 1s/step - loss: 2.0627 - accuracy: 0.6971 - val_loss: 2.0073 - val_accuracy: 0.6539
Epoch 3/10
32/32 [==============================] - 44s 1s/step - loss: 1.1046 - accuracy: 0.8951 - val_loss: 1.5246 - val_accuracy: 0.7412
Epoch 4/10
32/32 [==============================] - 44s 1s/step - loss: 0.6679 - accuracy: 0.9490 - val_loss: 1.2892 - val_accuracy: 0.7667
Epoch 5/10
32/32 [==============================] - 44s 1s/step - loss: 0.4455 - accuracy: 0.9804 - val_loss: 1.1587 - val_accuracy: 0.7873
Epoch 6/10
32/32 [==============================] - 43s 1s/step - loss: 0.3177 - accuracy: 0.9941 - val_loss: 1.0698 - val_accuracy: 0.7892
Epoch 7/10
32/32 [==============================] - 43s 1s/step - loss: 0.2375 - accuracy: 0.9961 - val_loss: 1.0055 - val_accuracy: 0.7971
Epoch 8/10
32/32 [==============================] - 44s 1s/step - loss: 0.1864 - accuracy: 0.9990 - val_loss: 0.9608 - val_accuracy: 0.8020
Epoch 9/10
32/32 [==============================] - 44s 1s/step - loss: 0.1477 - accuracy: 0.9990 - val_loss: 0.9274 - val_accuracy: 0.8039
Epoch 10/10
32/32 [==============================] - 44s 1s/step - loss: 0.1204 - accuracy: 0.9990 - val_loss: 0.8979 - val_accuracy: 0.8078
In [24]:
# Plotting the loss & accuracy values achieved during training for the training & validation set
plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
plt.title('Model Accuracy')
plt.ylabel('Accuracy')
plt.xlabel('Epoch')
plt.legend(['Train', 'Validation'], loc = 'upper left')
plt.show()

# Plotting the loss values for training & validation set
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('Model Loss')
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.legend(['Train', 'Validation'], loc = 'upper left')
plt.show()

Testing your Network¶

It's good practice to test your trained network on test data, images the network has never seen either in training or validation. This will give you a good estimate for the model's performance on completely new images. You should be able to reach around 70% accuracy on the test set if the model has been trained well.

In [25]:
# TODO: Print the loss and accuracy values achieved on the entire test set.
loss, accuracy = model.evaluate(testing_batches)

print('Loss on the testing data:', loss)
print('Accuracy on the testing data:', accuracy)

193/193 [==============================] - 131s 677ms/step - loss: 1.0210 - accuracy: 0.7694
Loss on the testing data: 1.0209532976150513
Accuracy on the testing data: 0.7693933844566345

Save the Model¶

Now that your network is trained, save the model so you can load it later for making inference. In the cell below save your model as a Keras model (i.e. save it as an HDF5 file).

In [26]:
# TODO: Save your trained model as a Keras model.
model.save('./first_model.h5')

Load the Keras Model¶

Load the Keras model you saved above.

In [27]:
# TODO: Load the Keras model
model2 = tf.keras.models.load_model('./first_model.h5', custom_objects = {'KerasLayer':hub.KerasLayer})
model2.summary()

Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 keras_layer_2 (KerasLayer)  (None, 1280)              2257984   
                                                                 
 dense_1 (Dense)             (None, 102)               130662    
                                                                 
=================================================================
Total params: 2,388,646
Trainable params: 130,662
Non-trainable params: 2,257,984
_________________________________________________________________

Inference for Classification¶

Now you'll write a function that uses your trained network for inference. Write a function called predict that takes an image, a model, and then returns the top $K$ most likely class labels along with the probabilities. The function call should look like:

probs, classes = predict(image_path, model, top_k)

If top_k=5 the output of the predict function should be something like this:

probs, classes = predict(image_path, model, 5)
print(probs)
print(classes)
> [ 0.01558163  0.01541934  0.01452626  0.01443549  0.01407339]
> ['70', '3', '45', '62', '55']

Your predict function should use PIL to load the image from the given image_path. You can use the Image.open function to load the images. The Image.open() function returns an Image object. You can convert this Image object to a NumPy array by using the np.asarray() function.

The predict function will also need to handle pre-processing the input image such that it can be used by your model. We recommend you write a separate function called process_image that performs the pre-processing. You can then call the process_image function from the predict function.

Image Pre-processing¶

The process_image function should take in an image (in the form of a NumPy array) and return an image in the form of a NumPy array with shape (224, 224, 3).

First, you should convert your image into a TensorFlow Tensor and then resize it to the appropriate size using tf.image.resize.

Second, the pixel values of the input images are typically encoded as integers in the range 0-255, but the model expects the pixel values to be floats in the range 0-1. Therefore, you'll also need to normalize the pixel values.

Finally, convert your image back to a NumPy array using the .numpy() method.

In [28]:
# TODO: Create the process_image function
def process_image(image):
    image = tf.convert_to_tensor(image, dtype=tf.float32)
    image = tf.image.resize(image,(image_size,image_size))
    image /= 255
    return image

To check your process_image function we have provided 4 images in the ./test_images/ folder:

  • cautleya_spicata.jpg
  • hard-leaved_pocket_orchid.jpg
  • orange_dahlia.jpg
  • wild_pansy.jpg

The code below loads one of the above images using PIL and plots the original image alongside the image produced by your process_image function. If your process_image function works, the plotted image should be the correct size.

In [29]:
from PIL import Image

image_path = './test_images/hard-leaved_pocket_orchid.jpg'
im = Image.open(image_path)
test_image = np.asarray(im)

processed_test_image = process_image(test_image)

fig, (ax1, ax2) = plt.subplots(figsize=(10,10), ncols=2)
ax1.imshow(test_image)
ax1.set_title('Original Image')
ax2.imshow(processed_test_image)
ax2.set_title('Processed Image')
plt.tight_layout()
plt.show()
In [35]:
from PIL import Image

image_path = './test_images/wild_pansy.jpg'
im = Image.open(image_path)
test_image = np.asarray(im)

processed_test_image = process_image(test_image)

fig, (ax1, ax2) = plt.subplots(figsize=(10,10), ncols=2)
ax1.imshow(test_image)
ax1.set_title('Original Image')
ax2.imshow(processed_test_image)
ax2.set_title('Processed Image')
plt.tight_layout()
plt.show()

Once you can get images in the correct format, it's time to write the predict function for making inference with your model.

Inference¶

Remember, the predict function should take an image, a model, and then returns the top $K$ most likely class labels along with the probabilities. The function call should look like:

probs, classes = predict(image_path, model, top_k)

If top_k=5 the output of the predict function should be something like this:

probs, classes = predict(image_path, model, 5)
print(probs)
print(classes)
> [ 0.01558163  0.01541934  0.01452626  0.01443549  0.01407339]
> ['70', '3', '45', '62', '55']

Your predict function should use PIL to load the image from the given image_path. You can use the Image.open function to load the images. The Image.open() function returns an Image object. You can convert this Image object to a NumPy array by using the np.asarray() function.

Note: The image returned by the process_image function is a NumPy array with shape (224, 224, 3) but the model expects the input images to be of shape (1, 224, 224, 3). This extra dimension represents the batch size. We suggest you use the np.expand_dims() function to add the extra dimension.

In [32]:
# TODO: Create the predict function
def predict (image_path, model, top_k):
    img = Image.open(image_path)
    test_image = np.asarray(img)
    processed_test_image = process_image(test_image)
    prob_preds = model.predict(np.expand_dims(processed_test_image, axis = 0))
    
    values,indices = tf.nn.top_k(prob_preds, k = top_k)
    probs = list(values.numpy()[0])
    classes = list(indices.numpy()[0])
    
    return probs, classes
In [36]:
image_path = './test_images/wild_pansy.jpg'
probs, classes = predict(image_path, model2, 5)

print('Probability', probs)
print('Classes', classes)

1/1 [==============================] - 0s 46ms/step
Probability [0.97961354, 0.005474086, 0.0027319123, 0.0020053473, 0.0012338902]
Classes [51, 63, 68, 33, 81]

Sanity Check¶

It's always good to check the predictions made by your model to make sure they are correct. To check your predictions we have provided 4 images in the ./test_images/ folder:

  • cautleya_spicata.jpg
  • hard-leaved_pocket_orchid.jpg
  • orange_dahlia.jpg
  • wild_pansy.jpg

In the cell below use matplotlib to plot the input image alongside the probabilities for the top 5 classes predicted by your model. Plot the probabilities as a bar graph. The plot should look like this:

You can convert from the class integer labels to actual flower names using class_names.

In [38]:
# TODO: Plot the input image along with the top 5 classes
import glob

files = glob.glob('./test_images/*')


for image_path in files:
    probs, classes = predict(image_path, model2,5)
    top_k_classes = [class_names[str(i+1)].title() for i in classes]
    fig, (ax1,ax2) = plt.subplots(figsize = (10,9), ncols = 2)
    ax1.imshow(Image.open(image_path))
    ax1.axis('off')
    ax1.set_title(f'True Flower Class: {image_path}')
    ax2.barh(np.arange(5), probs)
    ax2.set_aspect(0.1)
    ax2.set_yticks(np.arange(5))
    ax2.set_yticklabels(top_k_classes, size = 'small')
    ax2.set_title('Class Probability')
    ax2.set_xlim(0,1.1)
    plt.tight_layout()

1/1 [==============================] - 0s 50ms/step
1/1 [==============================] - 0s 53ms/step
1/1 [==============================] - 0s 49ms/step
1/1 [==============================] - 0s 48ms/step